Validation of Clear-Sky Global LAnd Surface Satellite (GLASS) Longwave Radiation Product

2020 ◽  
Author(s):  
Qi Zeng ◽  
Jie Cheng ◽  
Feng Yang
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Radiant Energy ◽  
Remote Sensing Data ◽  
Longwave Radiation ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Clear Sky ◽  
Global Land

<p>Surface longwave (LW) radiation plays an important rolein global climatic change, which is consist of surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN) and surface longwave net radiation (LWNR). Numerous studies have been carried out to estimate LWUP or LWDN from remote sensing data, and several satellite LW radiation products have been released, such as the International Satellite Cloud Climatology Project‐Flux Data (ISCCP‐FD), the Global Energy and Water cycle Experiment‐Surface Radiation Budget (GEWEX‐SRB) and the Clouds and the Earth’s Radiant Energy System‐Gridded Radiative Fluxes and Clouds (CERES‐FSW). But these products share the common features of coarse spatial resolutions (100-280 km) and lower validation accuracy.</p><p>Under such circumstance, we developed the methods of estimating long-term high spatial resolution all sky  instantaneous LW radiation, and produced the corresponding products from MODIS data from 2000 through 2018 (Terra and Aqua), named as Global LAnd Surface Satellite (GLASS) Longwave Radiation product, which can be free freely downloaded from the website (http://glass.umd.edu/Download.html).</p><p>In this article, ground measurements collected from 141 sites in six independent networks (AmerciFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE and TIPEX-III) are used to evaluate the clear-sky GLASS LW radiation products at global scale. The bias and RMSE is -4.33 W/m<sup>2 </sup>and 18.15 W/m<sup>2 </sup>for LWUP, -3.77 W/m<sup>2 </sup>and 26.94 W/m<sup>2</sup> for LWDN, and 0.70 W/m<sup>2 </sup>and 26.70 W/m<sup>2</sup> for LWNR, respectively. Compared with validation results of the above mentioned three LW radiation products, the overall accuracy of GLASS LW radiation product is much better. We will continue to improve the retrieval algorithms and update the products accordingly.</p>

scholarly journals Comparisons of Clear-Sky Outgoing Far-IR Flux Inferred from Satellite Observations and Computed from the Three Most Recent Reanalysis Products

2013 ◽  
Vol 26 (2) ◽  
pp. 478-494 ◽  
Author(s):  
Xiuhong Chen ◽  
Xianglei Huang ◽  
Norman G. Loeb ◽  
Heli Wei
Keyword(s):  
Land Surface ◽  
Radiant Energy ◽  
Longwave Radiation ◽  
Precipitable Water ◽  
Energy System ◽  
High Elevation ◽  
Radiation Budget ◽  
Distribution Models ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky

Abstract The far-IR spectrum plays an important role in the earth’s radiation budget and remote sensing. The authors compare the near-global (80°S–80°N) outgoing clear-sky far-IR flux inferred from the collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations in 2004 with the counterparts computed from reanalysis datasets subsampled along the same satellite trajectories. The three most recent reanalyses are examined: the ECMWF Interim Re-Analysis (ERA-Interim), NASA Modern-Era Retrospective Analysis for Research and Application (MERRA), and NOAA/NCEP Climate Forecast System Reanalysis (CFSR). Following a previous study by X. Huang et al., clear-sky spectral angular distribution models (ADMs) are developed for five of the CERES land surface scene types as well as for the extratropical oceans. The outgoing longwave radiation (OLR) directly estimated from the AIRS radiances using the authors’ algorithm agrees well with the OLR in the collocated CERES Single Satellite Footprint (SSF) dataset. The daytime difference is 0.96 ±2.02 W m−2, and the nighttime difference is 0.86 ±1.61 W m−2. To a large extent, the far-IR flux derived in this way agrees with those directly computed from three reanalyses. The near-global averaged differences between reanalyses and observations tend to be slightly positive (0.66%–1.15%) over 0–400 cm−1 and slightly negative (−0.89% to −0.44%) over 400–600 cm−1. For all three reanalyses, the spatial distributions of such differences show the largest discrepancies over the high-elevation areas during the daytime but not during the nighttime, suggesting discrepancies in the diurnal variation of such areas among different datasets. The composite differences with respect to temperature or precipitable water suggest large discrepancies for cold and humid scenes.

scholarly journals Developing Clear-Sky Flux Products for the Geostationary Earth Radiation Budget Experiment

2005 ◽  
Vol 44 (9) ◽  
pp. 1361-1374 ◽  
Author(s):  
J. M. Futyan ◽  
J. E. Russell
Keyword(s):  
Diurnal Cycle ◽  
Radiant Energy ◽  
Longwave Radiation ◽  
Energy System ◽  
Radiation Budget ◽  
Sampling Errors ◽  
Clear Sky ◽  
Temporal Sampling ◽  
Earth Radiation Budget ◽  
Earth Radiation

Abstract This paper describes the planned processing of monthly mean and monthly mean diurnal cycle flux products for the Geostationary Earth Radiation Budget (GERB) experiment. The use of higher-spatial-resolution flux estimates based on multichannel narrowband imager data to improve clear-sky sampling is investigated. Significant improvements in temporal sampling are found, leading to reduced temporal sampling errors and less dependence on diurnal models for the monthly mean products. The reduction in temporal sampling errors is found to outweigh any spatial sampling errors that are introduced. The resulting flux estimates are used to develop an improved version of the half-sine model that is used for the diurnal interpolation of clear-sky longwave fluxes over land in the Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) missions. Maximum outgoing longwave radiation occurs from 45 min to 1.5 h after local noon for most of the GERB field of view. Use of the ERBE half-sine model for interpolation therefore results in significant distortion of the diurnal cycle shape. The model that is proposed here provides a well-constrained fit to the true diurnal shape, even for limited clear-sky sampling, making it suitable for use in the processing of both GERB and CERES second-generation monthly mean clear-sky data products.

scholarly journals Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1848
Author(s):  
Chunjie Feng ◽  
Xiaotong Zhang ◽  
Yu Wei ◽  
Weiyu Zhang ◽  
Ning Hou ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Resolution ◽  
Land Surface ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Downward Longwave Radiation ◽  
Global Land

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

scholarly journals Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

2020 ◽  
Vol 12 (11) ◽  
pp. 1834
Author(s):  
Boxiong Qin ◽  
Biao Cao ◽  
Hua Li ◽  
Zunjian Bian ◽  
Tian Hu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hybrid Methods ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Estimation Methods ◽  
Mean Bias Error ◽  
Clear Sky ◽  
Surface Radiation Budget

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

scholarly journals The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

2020 ◽  
Vol 12 (6) ◽  
pp. 929 ◽  
Author(s):  
Nicolas Clerbaux ◽  
Tom Akkermans ◽  
Edward Baudrez ◽  
Almudena Velazquez Blazquez ◽  
William Moutier ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Radiant Energy ◽  
Longwave Radiation ◽  
Energy System ◽  
Radiation Budget ◽  
Climate Data ◽  
Night Time ◽  
Cloud Properties ◽  
Climate Monitoring ◽  
Orbital Drift

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

scholarly journals Assessing Stability of CERES-FM3 Daytime Longwave Unfiltered Radiance with AIRS Radiances

2012 ◽  
Vol 29 (3) ◽  
pp. 375-381 ◽  
Author(s):  
Xianglei Huang ◽  
Norman G. Loeb ◽  
Huiwen Chuang
Keyword(s):  
Radiant Energy ◽  
Longwave Radiation ◽  
Energy System ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky ◽  
The Difference ◽  
Spectrally Resolved ◽  
Independent Assessment ◽  
Flight Model ◽  
Bound Estimation

Abstract Clouds and the Earth’s Radiant Energy System (CERES) daytime longwave (LW) radiances are determined from the difference between a total (TOT) channel (0.3–200 μm) measurement and a shortwave (SW) channel (0.3–5 μm) measurement, while nighttime LW radiances are obtained directly from the TOT channel. This means that a drift in the SW channel or the SW portion of the TOT channel could impact the daytime longwave radiances, but not the nighttime ones. This study evaluates daytime and nighttime CERES LW radiances for a possible secular drift in CERES LW observations using spectral radiances observed by Atmospheric Infrared Sounder (AIRS). By examining the coincidental AIRS and CERES Flight Model 3 (FM3) measurements over the tropical clear-sky oceans for all of January and July months since 2005, a secular drift of about −0.11% yr−1 in the daytime CERES-FM3 longwave unfiltered radiance can be identified in the CERES Single Scanner Footprint (SSF) Edition 2 product. This provides an upper-bound estimation for the drift in daytime outgoing longwave radiation, which is approximately −0.323 W m−2 yr−1. This estimation is consistent with the independent assessment concluded by the CERES calibration team. Such secular drift has been greatly reduced in the latest CERES SSF Edition 3 product. Comparisons are conducted for the CERES window channel as well, and it shows essentially no drift. This study serves as a practical example illustrating how the measurements of spectrally resolved radiances can be used to help evaluate data products from other narrowband or broadband measurements.

scholarly journals Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

2017 ◽  
Vol 17 (22) ◽  
pp. 13559-13572 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Loretta J. Mickley ◽  
Eric M. Leibensperger ◽  
Michael J. Iacono
Keyword(s):  
United States ◽  
Radiative Transfer ◽  
Southeastern United States ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58–1.0 Wm−2 a−1 over the 2000–2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3–5.0  ×  10−3 a−1. Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol–radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000–2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as clear-sky may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000–2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm−2 a−1 at Goodwin Creek, MS, and +0.93 Wm−2 a−1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20–26 % of the seasonal (June–September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990–2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000–2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk.

scholarly journals A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

2021 ◽  
Author(s):  
Jianglei Xu ◽  
Shunlin Liang ◽  
Bo Jiang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Satellite Data ◽  
Land Surface ◽  
Radiant Energy ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget

Abstract. The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

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